#!/bin/bash

#当前路径,不需要修改
cur_path=`pwd`/../

#集合通信参数,不需要修改

export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0


# 数据集路径,保持为空,不需要修改
data_path=''
ckpt_path=''

#设置默认日志级别,不需要修改
export ASCEND_GLOBAL_LOG_LEVEL=3
#export ASCEND_DEVICE_ID=3

#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="PRIDNET_ID1275_for_TensorFlow"
#训练epoch
train_epochs=1
#训练batch_size
batch_size=256
#训练step
train_steps=`expr 1281167 / ${batch_size}`
#学习率
learning_rate=0.495

#TF2.X独有，需要模型审视修改
export NPU_LOOP_SIZE=${train_steps}

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_mix_precision"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_performance_1P.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		     data dump flag, default is False
    --data_dump_step		     data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
    --data_path		           source data of training
    -h/--help		             show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/test/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/test/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/test/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`

    elif [[ $para == --ckpt_path* ]];then
        ckpt_path=`echo ${para#*=}`
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1

fi

#训练开始时间，不需要修改
start_time=$(date +%s)

#进入训练脚本目录，需要模型审视修改
cd $cur_path/
for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $ASCEND_DEVICE_ID"
    export RANK_ID=$RANK_ID



    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/test/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/test/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
    fi




    # 绑核，不需要的绑核的模型删除，需要的模型审视修改
    let a=RANK_ID*12
    let b=RANK_ID+1
    let c=b*12-1
	
	
    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改

    python3 train_SIDD_Pyramid.py  \
	--dataset=${data_path} \
	--result=./result \
	--train_model_url=./train_model/ \
	--train_step=4001 \
	--chip="npu" >  ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log   2>&1
	
	python3 test_SIDD_Pyramid.py  \
	--val_dir=${data_path}/ValidationNoisyBlocksRaw.mat \
	--checkpoint_dir=./train_model/model-4000.ckpt \
	--result_dir=./res/  >  ${cur_path}/test/output/${ASCEND_DEVICE_ID}/test_${ASCEND_DEVICE_ID}.log   2>&1
	
	python3 test.py  \
	--val_dir1=${data_path}/ValidationGtBlocksRaw.mat \
	--val_dir2=./res/ValidationCleanBlocksRaw.mat  >  ${cur_path}/test/output/${ASCEND_DEVICE_ID}/val_${ASCEND_DEVICE_ID}.log   2>&1


done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
grep "Time ="  ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log  |awk '{print $8}'|tail -n +2 > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}_traintime.txt
cat  ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}_traintime.txt |awk '{sum+=$1} END {print "Avg = ",sum/NR}' > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}_traintime_avg.txt
TrainingTime=`grep 'Avg' ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}_traintime_avg.txt |awk '{print $3}'` 

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

##获取性能数据，不需要修改
#吞吐量
ActualFPS=`awk 'BEGIN{printf "%.2f\n", 256/'${TrainingTime}'}'`

train_acc=`grep 'SSIM:' ${cur_path}/test/output/$ASCEND_DEVICE_ID/val_$ASCEND_DEVICE_ID.log |awk '{print $2}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'Loss =' ${cur_path}/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk  '{print $5}'  > ${cur_path}/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' ${cur_path}/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_acc}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> ${cur_path}/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
